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A semi‐parametric time series approach in modeling hourly electricity loads
Author(s) -
Liu Jun M.,
Chen Rong,
Liu LonMu,
Harris John L.
Publication year - 2006
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.1006
Subject(s) - autoregressive integrated moving average , parametric statistics , nonparametric statistics , series (stratigraphy) , computer science , parametric model , data set , time series , nonlinear system , semiparametric model , sample (material) , set (abstract data type) , electricity , econometrics , mathematical optimization , statistics , mathematics , machine learning , artificial intelligence , engineering , paleontology , physics , chemistry , chromatography , quantum mechanics , electrical engineering , biology , programming language
In this paper we develop a semi‐parametric approach to model nonlinear relationships in serially correlated data. To illustrate the usefulness of this approach, we apply it to a set of hourly electricity load data. This approach takes into consideration the effect of temperature combined with those of time‐of‐day and type‐of‐day via nonparametric estimation. In addition, an ARIMA model is used to model the serial correlation in the data. An iterative backfitting algorithm is used to estimate the model. Post‐sample forecasting performance is evaluated and comparative results are presented. Copyright © 2006 John Wiley & Sons, Ltd.